The Internet has made it possible for people to connect and share information across the globe in ways previously undreamt of. Social media platforms, for example, enable people on opposite sides of the world to collaborate on ideas, discuss current events, or just share what they had for lunch. In the past, this spectacular resource has been somewhat limited to communications between users having a common natural language (“language”). Users have only been able to consume content that is in their language, or for which a content provider is able to determine an appropriate translation. Furthermore, the accuracy of language processing has been limited because machines have been unable to appropriately determine and apply contextual information for processing language.
Although language processing is a particular challenge, several types of language processing engines, such as parts-of-speech tagging engines, correction engines, and machine translation engines, have been created to address this concern. These language processing engines enable “content items,” which can be any item containing natural language including text, images, audio, video, or other multi-media, to be quickly classified, translated, sorted, read aloud, tagged, and otherwise used by machines. However, content items can be inaccurately processed due to rules and engines that do not account for the context of content items. For example, the word “lift” can mean “move upward” among speakers of American English (as that word is commonly used in America), whereas it can mean “elevator” for British English speakers. A content item including the phrase, “press the button for the lift,” could be translated into either “press the button for the elevator” or “press the button to go up.” In addition, machine translations of a content item are often based on dictionary translations and do not consider context, which often makes a significant difference such as in slang or colloquial passages.
The techniques introduced here may be better understood by referring to the following Detailed Description in conjunction with the accompanying drawings, in which like reference numerals indicate identical or functionally similar elements.